TOAST (Task-Oriented Adaptive Semantic Transmission) is a framework introduced to tackle multi-task optimization in dynamic wireless environments for 6G networks.
The framework includes components like adaptive task balancing using deep reinforcement learning, Low-Rank Adaptation (LoRA) mechanisms, and an Elucidating diffusion model for feature restoration.
Experiments show that TOAST outperforms baseline approaches in improving classification accuracy and reconstruction quality, especially in low Signal-to-Noise Ratio (SNR) conditions.
The framework aims to shift communication from bit-centric to semantic-aware, emphasizing task-relevant information in wireless transmissions.